TITLE: MACHINE LEARNING ENGINEER

• Manage machine learning models in production environments, ensuring smooth integration with existing systems
• Monitor model performance using established metrics (accuracy, precision, recall, F1 score, etc.) and identify potential issues like performance degradation, drift, or bias
• Participate in the response and resolution of production incidents promptly, minimizing downtime and impact on business operations
• Collaborate with stakeholders (data scientists, software engineers, operations) to diagnose and fix problems
• Maintain documentation for deployed models, including deployment logs, monitoring dashboards, and troubleshooting procedures
• Ensure model compatibility with new software versions and infrastructure changes.
• Archive and manage different model versions for auditability and rollback capabilities.
• Analyze logs, error messages, and other data to diagnose model issues.
• Utilize debugging tools and techniques specific to machine learning (profiling, feature importance analysis, anomaly detection).
• Reproduce and isolate issues in test environments before fixing them in production.
• Document bug fixes and lessons learned to improve future model development and maintenance.
• Analyze monitoring data proactively to identify trends and potential issues before they impact production.
• Stay up-to-date on new monitoring tools and techniques for machine learning systems.
• Report on model performance and health to stakeholders regularly.
• Stay up-to-date on advances in machine learning and best practices for production usage.
• Contribute to documentation and knowledge sharing within the team.

• Demonstrated proficiency in Python, SQL, and cloud platforms such as GCP, AWS, or Azure.
• Experience in DevOps practices for supporting and maintaining applications and products driven by Machine Learning (ML) and Artificial Intelligence (AI).
• Expertise in developing and optimizing ML pipelines.
• Understanding and experience in scaling solutions for ML and AI applications.
• Demonstrate experience in various ML frameworks such as Python, Scala or Java for debugging and developing minor enhancements to models.
• Proficiency in deploying and managing models using Kubernetes
• Experience in supporting products with capabilities including Deep Learning, Supervised, and Unsupervised techniques.
• Familiarity with implementing solutions using Python, Scala, or Java.
• Ability to utilize visualization tools for model monitoring and management in production.
• Experience in deploying and managing machine learning models using containerization tools such as Kubernetes, Docker, Fargate, etc.
• Documentation and communication skills for technical and non-technical stakeholders.
• Strong investigative and troubleshooting skills with regards to ML Pipelines
• Excellent written and verbal communication skills
• Bachelor’s degree in Computer Science or related discipline, or the equivalent in education and work experience

• Comfortable working with data scientists, data engineers, product owners and architects
• Background in Agile/Kanban development methodologies.
• Experience working within Agile Frameworks for ML and AI projects.